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Ships comprise the only and most important ocean transportation mode. Thus, ship detection is one of the most critical technologies in ship monitoring, which plays an essential role in maintaining marine safety. Optical remote-sensing images contain rich color and texture information, which is beneficial to ship detection. However, few optical remote-sensing datasets are open publicly due to the issue of sensitive data and copyrights, and only the HRSC2016 dataset is built for the ship-detection task. Moreover, almost all general object detectors suffer from the failure of multi-scale ship detection because of the diversity of spatial resolution and ship size. In this paper, we re-annotate the HRSC2016 dataset and supplement 610 optical remote-sensing images to build a new open source ship-detection benchmark dataset with rich multi-scale ship objects named the HRSC2016-MS dataset. In addition, we further explore the potential of a recursive mechanism in the field of object detection and propose a novel multi-scale ship-detection framework (MSSDet) in optical remote-sensing images. The success of detecting multi-scale objects depends on the hierarchical pyramid structure in the object-detection framework. However, the inherent semantic and spatial gaps among hierarchical pyramid levels seriously affect detection performance. To alleviate this problem, we propose a joint recursive feature pyramid (JRFP), which can generate semantically strong and spatially refined multi-scale features. Extensive experiments were conducted on the HRSC2016-MS, HRSC2016, and DIOR datasets. Detailed ablation studies directly demonstrated the effectiveness of the proposed JRFP architecture and also showed that the proposed method has excellent generalizability. Comparisons with state-of-the-art methods showed that the proposed method achieves competitive performance, i.e., 77.3%, 95.8%, and 73.3% mean average precision accuracy on the HRSC2016-MS, HRSC2016, and DIOR datasets, respectively. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) have attracted increasing attention in recent years because of their broad range of applications in city security, military reconnaissance, disaster rescue, and so on. As one of the critical algorithms in the field of artificial intelligence, object tracking greatly improves the working efficiency of UAVs. However, unmanned aerial vehicle (UAV) object tracking still faces many challenges. UAV objects provide limited textures and contours for feature extraction due to their small sizes. Moreover, to capture objects continuously, a UAV camera must constantly move with the object. The above two reasons are usual causes of object-tracking failures. To this end, we propose an end-to-end discriminative tracker called TMDiMP. Inspired by the self-attention mechanism in Transformer, a novel memory-aware attention mechanism is embedded into TMDiMP, which can generate discriminative features of small objects and overcome the object-forgetting problem after camera motion. We also build a UAV object-tracking dataset with various object categories and attributes, named VIPUOTB, which consists of many video sequences collected in urban scenes. Our VIPUOTB is different from other existing datasets in terms of object size, camera motion speed, location distribution, etc. TMDiMP achieves competitive results on our VIPUOTB dataset and three public datasets, UAVDT, UAV123, and VisDrone, compared with state-of-the-art methods, thus demonstrating the effectiveness and robustness of our proposed algorithm. [ABSTRACT FROM AUTHOR]

Aristolochic acids are known for nephrotoxicity, and implicated in multiple cancer types such as hepatocellular carcinomas demonstrated by recent studies. Natural products that are analogues to aristolochic acids have been constantly isolated from organisms; a larger chemical space of these compounds and a wider coverage of biological sources should be determined in consideration of the potential hazard of aristolochic acid analogues and the wide distribution of their biological sources in the nature. Therefore, we carried out an in silico research of naturally occurring aristolochic acid analogues and their biological sources, as a supplement to existing studies. The result shows a chemical space of 238 naturally occurring aristolochic acid analogues that are present in 175 species of biological sources including 44 traditional medicines. With the computational estimation for toxicity and the implication in hazard assessment of a biological source with the presence of aristolochic acid analogues, we propose that additional awareness should be raised to the public for avoidance of toxic species, especially those that are used as herbal medicines and easily accessible. [ABSTRACT FROM AUTHOR]

Solar photovoltaics (PV) deployment is not easy in dense urban areas because there is little space for the installation. Furthermore, tenants have few incentives to install PV panels because they frequently relocate, and most PV facilities are nonremovable. To address these factors, this study reports on an innovative model that collectively installed 260 W of mini-PV on the balconies of almost all the households in two high-rise apartment complexes in Seoul, South Korea. This project was unique in that it established energy commons in a community using private space. This study found that economic and social factors significantly influenced community-internal or micro factors, which in turn affected the success of the community energy project. Economic factors such as the expected economic benefit and residents paying no direct installation costs shaped the initial conditions for the commencement of the project. Leadership played a key role by speeding up the process, relieving residents' concerns and distrust. This study introduced an innovative community energy model that can be referenced by megacities and communities. It provides opportunities for enhancing awareness of energy transition via on-site energy production using renewable energy and allows even communities that have insufficient common space to build energy commons. [ABSTRACT FROM AUTHOR]

Chemically unstable natural products are prone to show their reactivity in the procedures of extraction, purification, or identification and turn into contaminants as so-called "artifacts". However, identification of artifacts requires considerable investments in technical equipment, time, and human resources. For revealing these reactive natural products and their artifacts by computational approaches, we set up a virtual screening system to seek cases in a biochemical database. The screening system is based on deep learning models of predicting the two main classifications of conversion reactions from natural products to artifacts, namely solvolysis and oxidation. A set of result data was reviewed for checking validity of the screening system, and we screened out a batch of reactive natural products and their probable artifacts. This work provides some insights into the formations of natural product artifacts, and the result data may act as warnings regarding the improper handling of biological matrixes in multicomponent extraction. [ABSTRACT FROM AUTHOR]

According to statistics, falls are the primary cause of injury or death for the elderly over 65 years old. About 30% of the elderly over 65 years old fall every year. Along with the increase in the elderly fall accidents each year, it is urgent to find a fast and effective fall detection method to help the elderly fall.The reason for falling is that the center of gravity of the human body is not stable or symmetry breaking, and the body cannot keep balance. To solve the above problem, in this paper, we propose an approach for reorganization of accidental falls based on the symmetry principle. We extract the skeleton information of the human body by OpenPose and identify the fall through three critical parameters: speed of descent at the center of the hip joint, the human body centerline angle with the ground, and width-to-height ratio of the human body external rectangular. Unlike previous studies that have just investigated falling behavior, we consider the standing up of people after falls. This method has 97% success rate to recognize the fall down behavior. [ABSTRACT FROM AUTHOR]

To improve the stability of the bridge structure, we detect bolts in the bridge which cause the symmetry failure of the bridge center. For data acquisition, bolts are small-scale objects under complex background in images, and their feature expression ability is limited. Due to those questions, we propose a new bolt positioning detection based on improved YOLOv5 for bridge structural health monitoring. This paper makes three major contributions. Firstly, according to the calibration anchor boxes of bolts, the size and proportion parameters of the initial anchor boxes are optimized by K-means++ clustering algorithm to solve the initial clustering problem of anchor boxes in object detection. Second, the hypercolumn (HC) technique fuses the low-level global features of the trunk and the high-level local features of three different scales to solve the problem of the inefficient distribution of anchors and insufficient extraction of classification features. In this way, we improve the detection accuracy and speed of bolt detection. Finally, we establish a dataset of bridge bolts through network collection and public datasets, including 1494 images. We compare and verify the new method in the collected bolt dataset. The experimental results show that the precision (P) of the improved YOLOv5x is up to 87.3%, and the average precision (AP) is up to 86.3%, which are 6.5% and 5.9% higher than the original YOLOv5x, respectively. [ABSTRACT FROM AUTHOR]

The safety and reliability of the power grid are related to national power security, economic development and people's daily life. The occurrence of extreme weather changes the external environment greatly. Including generators and transmission lines, many power grid units cannot resist such a huge attack and get damaged easily, which forces units to quit from the power grid running system for a while. Furthermore, if the number of influenced units is high enough, the whole power system will be destroyed by cascading failure caused by extreme weather. Aiming at dealing with the cascading failure emergencies, this paper is trying to improve the traditional power structural vulnerability model so that it can be used to discuss extreme weather and propose a theoretical topological model to help scholars measure the damage caused by extreme cases. Based on previous research in this field, this paper utilizes complex network knowledge to build the power grid topology model. Then, considering extreme cases and the three attack modes simulation process, this paper makes use of the characteristic parameters of the power grid topology model and designs an algorithm, according to the realistic situation of the propagation mechanism of cascading failure of the power grid model as well as extreme weather research. Finally, taking IEEE-30 and IEEE-118 node bus system as examples, which shows that the structural vulnerability method proposed in this paper can properly address the mechanism of unbalanced load of cascading failure of power grid units under extreme conditions and can provide theoretical reference for preventing and reducing the impact of extreme cases on power grid which improves the reliability of the power grid. [ABSTRACT FROM AUTHOR]

The large-scale interconnection of the power grid has brought great benefits to social development, but simultaneously, the frequency of large-scale fault accidents caused by extreme weather is also rocketing. The power grid is regarded as a representative complex network in this paper to analyze its functional vulnerability. First, the actual power grid topology is modeled on the basis of the complex network theory, which is transformed into a directed-weighted topology model after introducing the node voltage together with line reactance. Then, the algorithm of weighted reactance betweenness is proposed by analyzing the characteristic parameters of the power grid topology model. The product of unit reliability and topology model's characteristic parameters under extreme weather is used as the index to measure the functional vulnerability of the power grid, which considers the extreme weather of freezing and gale and quantifies the functional vulnerability of lines under wind load, ice load, and their synergistic effects. Finally, a simulation using the IEEE-30 node system is implemented. The result shows that the proposed method can effectively measure the short-term vulnerability of power grid units under extreme weather. Meanwhile, the example analysis verifies the different effects of normal and extreme weather on the power grid and identifies the nodes and lines with high vulnerability under extreme weather, which provides theoretical support for preventing and reducing the impact of extreme weather on the power grid. [ABSTRACT FROM AUTHOR]

Traffic congestion is a major problem in today's society, and the intersection, as an important hub of urban traffic, is one of the most common places to produce traffic congestion. To alleviate the phenomenon of congestion at urban traffic intersections and relieve the traffic pressure at intersections, this paper takes the traffic flow at intersections as the research object and adopts the swarm intelligent algorithm to establish an optimization model of intersection traffic signal timing, which takes the average delay time of vehicles, the average number of stops of vehicles and the traffic capacity as the evaluation indexes. This model adjusts the intersection traffic signal timing intelligence according to the real-time traffic flow and carries out simulation experiments with MATLAB. Compared with the traditional timing schemes, the average delay time of vehicles is reduced by 10.25%, the average number of stops of vehicles is reduced by 24.55%, and the total traffic capacity of the intersection is increased by 3.56%, which verifies that the scheme proposed in this paper is effective in relieving traffic congestion. [ABSTRACT FROM AUTHOR]